Harnessing Computational Approaches in Drug Discovery: From Target Identification to Clinical Translation

The use of computer-based methods has changed the way new medicines are discovered today. It has made the process quicker, cheaper, and more accurate from finding a target to developing a candidate for clinical trials. Traditional methods of discovering drugs often face problems like high expenses, long time frames, and low success in clinical testing. On the other hand, computer-based techniques such as molecular modeling, bioinformatics, virtual screening, quantitative structure activity relationship modeling, and machine learning help scientists predict how drugs will interact with their targets, enhance lead compounds, and estimate important properties like how a drug is absorbed, distributed, metabolized, eliminated, and its potential toxicity. Design strategies that focus on structures and those that focus on the interactions of molecules improve the chances of creating effective drug candidates with fewer side effects. By utilizing extensive data from fields like genomics, proteomics, and transcriptomics, these computer-based methods help find new treatment targets and speed up the process from finding a hit to creating a lead compound. Advanced tools like molecular dynamics simulations, density functional theory, and network pharmacology offer a better understanding of how drugs bind and interact with multiple targets. In addition, combining these methods with experimental techniques helps confirm computer predictions, bridging the gap between theoretical work and reallife application. Recent improvements in artificial intelligence and deep learning are broadening the possibilities for computeraided drug design, aiding in drug repurposing, new design, and modeling predictions. Altogether, these advancements are lessening failure rates and enhancing the effectiveness of drug development in the early and later stages. Therefore, computerbased methods are essential for shaping the future of personalized medicine and precise therapeutic approaches.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-09-16
DOI
https://doi.org/10.5281/zenodo.17128638
Primary Topic
Geochemistry and Geologic Mapping
Type
article
Field-Weighted Citation Impact
0.00
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article

Harnessing Computational Approaches in Drug Discovery: From Target Identification to Clinical Translation

Zenodo (CERN European Organization for Nuclear Research)
Geochemistry and Geologic Mapping
article

Harnessing Computational Approaches in Drug Discovery: From Target Identification to Clinical Translation

article en

Abstract

The use of computer-based methods has changed the way new medicines are discovered today. It has made the process quicker, cheaper, and more accurate from finding a target to developing a candidate for clinical trials. Traditional methods of discovering drugs often face problems like high expenses, long time frames, and low success in clinical testing. On the other hand, computer-based techniques such as molecular modeling, bioinformatics, virtual screening, quantitative structure activity relationship modeling, and machine learning help scientists predict how drugs will interact with their targets, enhance lead compounds, and estimate important properties like how a drug is absorbed, distributed, metabolized, eliminated, and its potential toxicity. Design strategies that focus on structures and those that focus on the interactions of molecules improve the chances of creating effective drug candidates with fewer side effects. By utilizing extensive data from fields like genomics, proteomics, and transcriptomics, these computer-based methods help find new treatment targets and speed up the process from finding a hit to creating a lead compound. Advanced tools like molecular dynamics simulations, density functional theory, and network pharmacology offer a better understanding of how drugs bind and interact with multiple targets. In addition, combining these methods with experimental techniques helps confirm computer predictions, bridging the gap between theoretical work and reallife application. Recent improvements in artificial intelligence and deep learning are broadening the possibilities for computeraided drug design, aiding in drug repurposing, new design, and modeling predictions. Altogether, these advancements are lessening failure rates and enhancing the effectiveness of drug development in the early and later stages. Therefore, computerbased methods are essential for shaping the future of personalized medicine and precise therapeutic approaches.

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 36%
Geochemistry and Geologic Mapping
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Harnessing Computational Approaches in Drug Discovery: From Target Identification to Clinical Translation · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS